AI agents vs. AI assistants

To better understand these differences, imagine you are a movie star or star footballer. You probably have an agent and an assistant. Your assistant does tasks for you, based on your requests. They might make dinner reservations, pick up the dry cleaning, organize fan mail and help maintain your calendar.

Your agent is different. They are using their expertise day and night to maximize your opportunities and income. They can act based on your prompts—maybe a product you’d love to endorse—but they don’t need prompts to continue to do their job. In fact, your Hollywood agent probably supports you in ways you wouldn’t even know to ask.

The key difference between an artificial intelligence (AI) assistant and an AI agent is similar. AI assistants are reactive, performing tasks at your request. AI agents are primarily proactive, autonomously planning and taking actions to achieve a defined goal using the tools and permissions available to them.

Together, assistants and agents elevate great performers, making them or keeping them stars. In much the same way, AI assistants and AI agents can make individual workers and businesses better by performing simple and complex tasks.

An AI assistant is an intelligent application that understands natural language commands and uses a conversational AI interface to complete tasks for a user. Many modern virtual assistants, such as Amazon’s Alexa and Apple’s Siri, rely on these capabilities to enhance user interactions.

The first AI assistants relied mostly on rule-based instructions, preprogrammed responses and predefined tasks. Today, AI assistants are predominantly powered by machine learning algorithms and foundation models. Generative AI (gen AI) enables them to understand natural language, generate content and have flexibility in their responses to user requests.

AI assistants are typically powered by a foundation model (for example, IBM® Granite™, Meta’s Llama models or OpenAI’s models). Large language models (LLMs) are a subset of foundation models that specialize in text-related tasks. They enable assistants to understand queries submitted by humans and offer relevant information, suggestions or next-step actions.

This helps organizations simplify access to information, automate repetitive tasks and streamline complicated workflows. In business, AI assistants also assist with data analysis, allowing users to efficiently extract insights.

Key features of AI assistants include:

• Conversational AI: LLM-based AI assistants can use natural language processing (NLP) to communicate with users through a chatbot interface. AI chatbot examples include Microsoft Copilot, ChatGPT, Salesforce Agentforce and IBM watsonx™ Assistant. These assistants integrate with application programming interfaces (APIs) to expand their capabilities.
  
  
• Prompts: AI assistants begin with a user prompt and generally rely on user direction throughout a task.
  
  
• Recommendation: An AI assistant can suggest information or actions based on data it can access. Users should review outputs for accuracy. The AI assistant waits for permission to act on the recommendation.
  
  
• Tuning: Users can adapt AI models to more specific tasks without building a new AI model from scratch. Prompt tuning and instruction tuning help tailor how a model responds to tasks or requests. Retrieval-augmented generation (RAG) gives an assistant access to current information from trusted documents, databases or an organization’s knowledge base without changing the underlying AI model.

AI assistants have several limitations:

• They require defined prompts to take action. While AI assistants can use tools to perform tasks, their capabilities are limited to predefined functions they have been equipped and trained to handle. For example, an AI assistant can use a spreadsheet to generate a table comparing “x vs. y,” but it cannot independently decide to create such a comparison without a specific prompt.
  
  
• They do not necessarily have persistent memory. AI assistants can be tailored to fit a user’s needs, but they do not inherently retain information from past user interactions. The AI models that power assistants do not continuously learn or evolve based on usage; instead, improvements occur only when the developers release updated versions.
  
  Some AI assistants can remember information between conversations, such as a user’s preferences or previous requests, to provide more personalized responses. But this isn’t true learning. The assistant’s underlying AI model does not change or improve based on individual user interactions; it is only updated when its developers release a new or retrained version.

AI has moved beyond answering questions to getting work done. To quote Elvis Presley, “A little less conversation, a little more action, please.”

Enter AI agents. An AI agent refers to a system or program that can autonomously complete tasks on behalf of users or other systems by planning its own workflow and using available tools.

More autonomous and connected than AI assistants, AI agents can perform a wider range of functions beyond natural language interaction. These include decision-making, problem-solving, interacting with external environments and executing actions. 

Whereas AI assistants typically need ongoing user direction, AI agents can continue working independently after an initial kickoff prompt. They evaluate assigned goals, break tasks into subtasks and develop their own workflows to achieve specific goals.

These agents are deployed across various enterprise applications, from software design and IT automation to customer service, business process automation (BPA) and code-generation workflows. Using advanced NLP from LLMs, AI agents comprehend user inputs step-by-step, strategize their actions and determine when to call on external tools.

• Greater autonomy: After an initial prompt, AI agents can continue working without further input, reducing the need for human intervention at every stage. Unlike assistants, which suggest actions for users to approve, AI agents leverage multicomponent autonomy to independently reason, decide and problem-solve using external data sets and tools.
  
  AI agents’ ability to move beyond a purely chat-based interface enables proactive planning, decision-making and task execution, which helps employees save time by handling complex workflows more independently. Newer models are improving reasoning capabilities to support this.
  
  
• Connectivity: Agents unify various capabilities into a single workflow, eliminating bottlenecks that arise from disconnected systems. By integrating seamlessly with external applications, data sources and other AI models, they enhance productivity while reducing friction between different components of a process. This connectivity allows them to operate across a company’s broader tech ecosystem.
  
  
• Decision-making and action: The ability to call on tools by itself does not make an LLM an agent. AI agents can also act autonomously and decide which tools to use, when to use them and how to combine them to achieve a goal.
  
  Powered by foundation models, AI agents extend their capabilities by interacting with external applications, databases and services, allowing them to complete tasks that go beyond the capabilities of the foundation model alone. They analyze problems, break them into subtasks and plan their next steps autonomously.
  
  This makes them effective for handling complex, ambiguous problems. Some agents, such as Anthropic’s Claude, even demonstrate computer use, where an LLM can click, type and operate a computer to complete tasks.
  
  
• Persistent memory and adaptive learning: Compared to AI assistants, AI agents are better able to retain context and improve their performance over time. They can remember previous actions, conversations and outcomes, allowing them to refine future plans and avoid repeating unsuccessful approaches.
  
  Some AI agents can incorporate feedback to improve future performance. While an agent may remember and adapt based on past interactions, the underlying AI model itself is not continually retrained. This combination of memory and adaptability helps AI agents respond more effectively to changing goals and environments.
  
  
• Task chaining: AI agents don’t complete tasks in isolation—they break complex workflows into smaller, manageable steps. AI agents identify dependencies between tasks, which help ensure that each step logically flows into the next. This ability enables structured execution across multi-step processes and makes automation more dynamic.
  
  
• Team play: AI agents often specialize in specific tasks—one may excel at fact checking, while another is better at research. These agents can collaborate, forming teams to tackle complex challenges together. IBM currently supports AI agents written in LangChain and LlamaIndex. Instead of being developer-heavy, IBM’s framework enables users to compose and edit AI agents in a low-code or no-code environment.

AI agents and AI assistants offer numerous benefits, from optimizing workflows to enhancing user experience. 

• Complementary AI solutions: AI agents specialize in performing specific or complex tasks autonomously, while AI assistants excel at understanding and interacting with users through natural language. Together they create more powerful and intuitive AI solutions.
  
  
• Optimized workflows and increased productivity: AI assistant, AI agents and gen AI streamline processes, automate routine tasks and help users solve problems, improving productivity and overall efficiency across a wide range of business functions.
  
  
• Enhanced user experience: AI assistants provide interactive support and can personalize responses by using conversation history, user preferences and feedback to offer more relevant interactions.
  
  
• Autonomous operations and scalability: AI agents can operate independently, manage multiple tasks simultaneously and scale to handle complex processes with minimal human intervention.
  
  
• Improved task management and collaboration: AI agents and AI assistants can work together to coordinate complex workflows. AI agents can orchestrate multi-step tasks, while AI assistants provide conversational support, summarize results and help users review outputs. Together, they improve coordination and task management.
  
  
• Improved integration: As AI technologies evolve, conversational assistants and autonomous agents can work more closely together, enabling smoother task handoffs, better integration with business systems and faster, more consistent results.

AI assistants improve customer experience by providing real-time support across chat, voice and email. They handle common customer inquiries, guide users through self-service options, and escalate complex issues when needed. Using NLP, they personalize interactions, recommend products and help customers complete transactions quickly. The anytime availability improves customer satisfaction and reduces costs.

AI agents take customer experience and customer support further by adapting to user behavior in real time. Unlike AI assistants that primarily respond to user requests, AI agents can plan actions, use tools and coordinate multi-step interactions, whether it’s simulating job interviews or handling complex support issues with limited human intervention. They work across websites, apps and IoT devices to create smoother and more personalized user experiences.

AI assistants provide secure, real-time banking support by handling balance inquiries, fraud alerts and loan applications. They also help customers manage their finances by analyzing spending habits and offering personalized budgeting advice.

AI agents proactively help prevent fraud by monitoring transactions in real-time, detecting and flagging suspicious activity before it escalates. Unlike assistants that just send fraud alerts, AI agents adjust security protocols, refine risk models and coordinate with fraud detection systems to stay ahead of emerging threats.

In trading and investment, AI agents analyze market trends, recommend trades and support or automate certain portfolio management decisions while operating within the controls and regulatory requirements of the organization.

AI assistants play a key role in human resources (HR) process automation, helping organizations streamline recruitment by generating job descriptions, screening and organizing resumes and drafting personalized messages. Beyond hiring, they assist in onboarding by guiding new employees through policies, benefits and training materials.

AI agents take HR automation further by managing and optimizing talent acquisition, employee engagement and workforce planning. They screen candidates, schedule interviews and refine hiring strategies using historical and real-time data while supporting human decision-making. For performance management, AI agents analyze employee feedback, detect trends and recommend training programs. They also automate onboarding, benefits administration and compliance tracking, making HR operations more data-driven and efficient.

AI assistants help to improve patient experiences and streamline administrative tasks. They answer patient questions in real-time, assist with appointment scheduling, billing and prescription refills and provide self-service access to medical records.

AI assistants help doctors by summarizing patient histories and flagging urgent cases. AI assistants also help organize documentation, helping to ensure formatting remains consistent and easier to access.

AI agents support medical decision-making in complex environments. They analyze real-time patient data, help prioritize cases and recommend potential actions for clinical review. AI agents also help optimize drug supply management and predict shortages.

There are risks and limitations with AI-powered technologies to consider. LLMs, which power many AI assistants and AI agents, can produce incorrect, inconsistent or fabricated responses known as hallucinations. They may also behave unpredictably when prompt or input data change, producing unreliable results or causing tasks to fail.

If an AI agent has trouble creating an effective plan or evaluating its progress, it may get stuck repeating the same actions or fail to complete its assigned task. And because AI agents rely on external applications, services and data sources, they must deal with any changes to those tools which might cause workflows to fail or produce unexpected results.  

AI assistants, on the other hand, have fewer dependencies than AI agents and typically execute simpler workflows, making them easier to deploy and manage. But assistants that integrate with external tools can also be affected by changes to those systems.

For harder tasks, AI agents require careful design, testing and configuration. They might also take longer to complete tasks and can be more expensive to deploy and operate than AI assistants.

Today’s foundation models are not yet consistently reliable enough to operate as fully autonomous agents in many real-world situations. We are still in the early days of understanding and seeing what AI agents can do. The future of AI might see expanded self-guided applications of AI technology. But for most current business applications, human oversight remains important to monitor performance and intervene when necessary.